The current public switched telephone network (PSTN) was implemented as a highly reliable, robust, and efficient system for transporting voice traffic. The PSTN has now been burdened with additional types of traffic for which the PSTN was not designed to transport (e.g., Internet, file transfer, video, fax, etc.). The current narrowband synchronous transfer mode (STM) telephony system will have to be replaced by or evolve into a broadband network to preserve the integrity of the system and accommodate the new services.
The asynchronous transfer mode (ATM) protocol has been selected as the core switching protocol for emerging broadband networks. ATM is an elegant protocol that has the desirable ability to multiplex voice, video, and data and to transmit information on the same communications channel at very high speeds. As used herein, ATM refers to a connection-oriented protocol in which bandwidth is allocated when the originating end user requests a connection. This allows ATM to efficiently support a network's aggregate demand by allocating bandwidth on demand based on immediate user need.
Problems have been encountered in modeling traffic on an ATM network, which has complicated the design and analysis of ATM networks. For a network to be properly sized and provisioned, the design engineer must thoroughly understand the traffic load and the behavior of that traffic load over time. Traditionally, STM networks were based on the Poisson model. Random number generators were used to produce streams of numbers, representative of real network interarrival times, and which are based on the Poisson model. However, this model is unable to accurately characterize the “bursty” nature of ATM network traffic. Burstiness is present in a traffic process if the arrival points appear to form visual clusters; that is, the packets have runs of several short interarrival times (i.e., the time interval between the receipt of successive packets at a specified destination from a specific source) followed by a relatively long one. As will be appreciated, voice and video packets in ATM networks are typically given a higher priority than data packets in routing or switching the packets for processing. Accordingly, data packets can have significantly longer packet interarrival times than voice or video packets.
Other models have been considered in modeling ATM traffic using random number generators, including the Markov Modulated model, the Transform Expand Sample model, the Autoregressive model, the Fluid model, and the Self-similar model. Although these models have been found to have varying degrees of success for modeling Ethernet traffic (which, like ATM networks, uses a packet-based protocol), they have been largely unsuccessful in characterizing the bursty nature of ATM traffic.
The failure of these models is in part due to the differences between ATM networks and other type of packet networks. For example, ATM is a connection-oriented protocol with a fixed length packet size. This contrasts with Ethernet which is a connectionless protocol with variable length packet size. Variable packet sizes give rise to a Gaussian (normal) or exponential probilistic distribution of packet interarrival times.